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Cracks in concrete structures are one of the most important indicators of structural damage, and it is a necessity to detect and measure cracks for ensuring safety and integrity of concrete structures. The widely practised approach in inspecting the structures is by performing visual inspections followed by manual estimation of crack widths. This approach is not only time-consuming, laborious, and time-intensive but also prone to subjective errors and inefficient. To address these issues, we propose a novel deep learning framework for detecting cracks and then estimating crack widths in concrete surface images. Our framework handles both small- and large-sized images and provides a prediction of crack width at locations specified by the user. The proposed framework uses Attention Recurrent Residual U-Net (Attention R2U-Net) with Random Forest regressor to predict crack width with the mean prediction error of ±0.31 mm for crack widths varying from 0 to 8.95 mm and produces the lowest absolute maximum error of 1.3 mm. Our model has a coefficient of determination ( R2) of 0.91, showing a non-linear mapping function with low prediction errors. We compare our model with a combination of four other segmentation models and regression models. Our proposed model has superior performance compared to other models, and one can easily adopt our framework to a variety of Structural Health Monitoring applications using Internet of Things sensors.

The environmental impact of traditional Ordinary Portland Cement (OPC) concrete is a significant problem that requires urgent solutions in the construction industry. The development of geopolymer concrete is one of the most significant breakthroughs in the process of replacing OPC concrete. Through comprehensive experimental and numerical analyses, this study investigates the structural behaviours of large-scale steel reinforced geopolymer concrete beams (GCBs) made from low-calcium fly ash and ground granulated blast furnace slag (GGBFS). Firstly, small-scale experiments were carried out to investigate the effects of water/binder and activator/binder ratios on the mechanical properties of geopolymer concrete (e.g. elastic modulus, compressive strength, direct tensile strength). The experimental results show that the mechanical properties of geopolymer concrete increases with increasing the activator/binder ratio and decreasing the water/binder ratio. Secondly, three groups of GCBs with different steel reinforcement ratios (D1-0.41%, D2-0.75%, and D3-1.5%) were made and four-point bending tests were conducted. The same mix proportion (water/binder of 0.45 and activator/binder of 0.08) with the compressive strength of 39.1 MPa, elastic modulus of 32.0 GPa, and the direct tensile strength of 3.06 MPa, was used for the three groups. The obtained moment–curvature results, which consists of three distinct stages (linear elastic, tension cracking of GCBs, and steel yielding), show that the three groups (D1-D3) behave in a ductile manner. Moreover, the moment capacity of GCBs increases when the steel reinforcement ratio increases (D1-21.5 kNm, D2-44.2 kNm and D3-83.6 kNm). Finally, nonlinear, three-dimensional finite element (FE) analysis based on the damage plasticity constitutive law was developed to capture and validate the structural behaviour of GCBs from the experiments. Numerical results indicate that the developed FE models accurately capture the structural behaviours (moment–curvature and cracking behaviour) of GCBs. The discrepancies between the numerical and experimental moment–curvature results are from 1 to 5% for the tensional cracking and yielding points. Therefore, the developed FE models can be used as an effective tool for the further development and design of geopolymer concrete structures.

PurposeBuilding information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.Design/methodology/approachThis paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.FindingsFour domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.Originality/valueThis paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

Retrofitting concrete with fiber-reinforced polymer (FRP) wrap has been considered as an effective method to compensate for the loss of the compressive strength of the concrete at an elevated temperature. This paper presents a study on the axial compressive behavior of heat-damaged steel fiber-reinforced concrete strengthened with carbon FRP (CFRP) wraps. To this effect, steel fibers were added to two concrete mixtures with volume fractions of 0.5% and 1%. The effect of full and partial wrapping with CFRP composites on the compressive behavior of the heated concrete was examined. A stress–strain model was also proposed to predict the behavior of CFRP-confined post heated steel fiber-reinforced concrete. It was found that heated concrete wrapped with CFRP layers exhibits a higher compressive strength, peak axial strain, maximum volumetric strain, toughness and relative energy absorption than that of unwrapped concrete. The results also showed that the exposure of concrete to the heat leads to a slight decrease in the ascending and descending slopes of the axial stress–strain relationship. The comparison between the model predictions and experimental results revealed that the proposed model accurately predicts the axial stress-axial strain and axial stress-lateral strain of the heated concrete. These results are promising and demonstrate the benefits of full and partial FRP wrapping in the development of high-performance structural members subjected to an elevated temperature.

Contrary to conventional buildings, the structural robustness of modular high-rise buildings has not been studied due to the lack of the numerical and experimental studies and design guides. To gai...

The usage of antimicrobials in livestock production is a driver for antimicrobial resistance worldwide. Reducing the use of antibiotics in the animal sector is a priority and requires a change in practices. Vietnam has diverse husbandry and antimicrobial use practices. The objective of this study was to determine the socio-economic and technical factors associated with antibiotic usage patterns on chicken farms in the north and south of Vietnam. Semi-structured interviews (n = 34) and on-farm questionnaires (n = 125) were conducted to collect socioeconomic, technical, biosecurity, health management, and antibiotic usage data. Using Multivariate Corresponding Analysis, we identified three production systems (A, B, C) and three patterns of antibiotic usage (1, 2, 3). Group A raised indoor exotic chickens in an intensive setting and was associated with group 1, which used antibiotics according to company recommendations for both treatment and prevention. Group C raised freerange chickens for their own consumption and was associated with group 2, which used antibiotics according to drugstore advice for treatment. Finally, group B was a market-oriented, semi-confined system associated with group 3, which practiced experience-based antibiotic use and overuse. Farms in the south of Vietnam were associated with group 3 and those in the north with group 2. The prediction of antibiotic usage patterns based on farming practices could lead to the identification of a group of farms to be targeted in order to foster the more prudent use of antibiotics in Vietnam.

Based on the notion of Stark units, we present a new approach that obtains refinements of log-algebraic identities for Anderson $t$-modules. As a consequence, we use our techniques to recover many earlier results and prove stronger results in some cases. Further, we devise a direct and conceptual way to get logarithmic interpretations for multiple zeta values in positive characteristic. This generalizes the work of Anderson and Thakur for Carlitz zeta values.